performance-optimization-principles

name	performance-optimization-principles
description	Performance principles: measure-first methodology, data structures, caching, batching, when to stop. For hands-on profiling, use perf-optimization skill.
user-invocable	false

Performance Optimization Principles

Measure First

1. Profile to find actual bottleneck (don't guess)
2. Find 20% of code consuming 80% of resources
3. Optimize that specific bottleneck
4. Measure again — verify with benchmarks
5. Repeat only if still not meeting goals

Don't optimize: "fast enough," rarely executed, no measurable problem.

Data Structures

• Hash map: O(1) lookup, unordered
• Array: O(1) index, O(n) search, ordered
• Tree: O(log n) ops, sorted
• Set: O(1) membership, unique

Wrong choice = degradation: array for lookups (O(n) vs O(1)), list for sorted data (O(n log n) vs O(log n)).

Avoid Premature Abstraction

Costs: runtime (indirection, virtual dispatch), cognitive (layers), maintenance (ripple). Start concrete, abstract when pattern emerges. No "future flexibility" without evidence.

Techniques

• Caching: store expensive results, TTL, proper invalidation
• Lazy loading: compute/load on-demand
• Batching: N queries → 1 query, batch INSERTs, pipeline Redis
• Async I/O: don't block, concurrent I/O ops
• Connection pooling: see @.gemini/skills/resources-and-memory-management/SKILL.md

Checklist

• Measured performance problem (not guessed)?
• Profiled for actual bottleneck?
• Appropriate data structures for access pattern?
• Expensive ops cached with invalidation?
• Batch ops instead of N+1?
• Non-blocking I/O where appropriate?
• Measured improvement after optimization?

Related

• Resources @.gemini/skills/resources-and-memory-management/SKILL.md
• Concurrency Mandate GEMINI.md § Concurrency and Threading Mandate
• Concurrency Principles @.gemini/skills/concurrency-and-threading-principles/SKILL.md